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PBL Teaching Mode Promotes Deep Learning For Senior One Chemistry Practical Research

Posted on:2021-03-04Degree:MasterType:Thesis
Country:ChinaCandidate:Y L DingFull Text:PDF
GTID:2427330611987252Subject:Education
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Since the reform of the new curriculum,various teaching modes and teaching concepts have sprung up.Such as deep learning,cooperative learning,autonomous learning,and inquiry learning.Among them,deep learning is an effective learning method to improve students' thinking ability in chemistry disciplines and cultivate high-quality creative talents.The PBL teaching model is highly compatible with deep learning in many aspects such as purpose and method.After the introduction of junior high school chemistry,the first grade students have entered a new stage of learning.Compared with the chemistry knowledge of junior high school,the content of high school knowledge is more extensive,the ability requirements are higher.Students' deep learning is promoted through appropriate teaching models in the compulsory courses of the first year of high school.Students' good quality of subject thinking and learning habits are cultivated.It can lay a good foundation for the study of subsequent elective courses.So the deep learning of high school students was promoted by researching and carrying out the PBL teaching model in this article.Students become masters of the classroom.Students' learning enthusiasm was fully mobilized.Students' learning habits and thinking modes were cultivated.Various high-level abilities such as students' critical thinking,problem solving capabilities,metacognitive ability and migration ability were also developed.The development,meaning and characteristics of PBL teaching mode and deep learning were clarified by combing the domestic and foreign literature in this paper.Relational surveys were conducted by using questionnaires and interviews to investigate teachers and students.Based on the reference of PBL teaching steps at home and abroad and the TQVC concept cognitive model,a practical process was proposed to promote students' deep learning under the PBL mode.Cases were developed.Teaching was implemented.After the practice was completed,the changes of the students were evaluated through the collation,analysis,and comparison of the pretest data and post test data.Finally got inspiration.The paper was divided into five parts.The first part included the introduction,the development status at home and abroad,the purpose,significance,ideas and methods of the research.The second part included four major theoretical foundations,namely "constructivism theory","variation teaching theory","distributed cognitive theory" and "subjective education theory".The relevant concepts of “PBL teachingmodel” and ”deep learning” were defined.The third part,the deep learning ability of the freshman students was investigated by using the student questionnaire.The teachers' views of deep learning was investigated by using teacher interviews.The survey results were used as pretest data.In the fourth part,six strategies were proposed to promote students' deep learning.The practical process of PBL teaching mode was designed.Three teaching cases were selected to show including "ionization of electrolyte","mutual conversion of different valence iron ions" and "sulfur dioxide".Finally,the practical results was analyzed from five aspects.Student questionnaires after practice,chemical test results,classroom performance evaluation form,teacher interviews after practice and student interviews.Three teaching inspirations were drew.The fifth part was the conclusion and prospect of the research.Through this research,six strategies for promoting deep learning for students were proposed.The practical process was clarified.Three teaching cases were shown.Conclusions were drawn from five perspectives,indicating that deep learning for students was promoted by the PBL teaching model.Finally three revelations were concluded.A certain reference for senior high school chemistry teaching was provided.
Keywords/Search Tags:PBL teaching mode, high school chemistry, deep learning
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